1. Field
The present disclosure relates to magnetic resonance imaging (MRI) apparatuses and methods of processing images on the MRI apparatuses, and more particularly, to MRI apparatuses and methods of distinguishing and quantifying properties of an object via MRI.
2. Description of the Related Art
An MRI apparatus uses a magnetic field to capture an image of a subject, and is widely used in the accurate diagnosis of diseases because it shows stereoscopic images of bones, lumbar discs, joints, and nerve ligaments at desired angles.
The MRI apparatus is configured to acquire MR signals and reconstruct the acquired MR signals into an image to be output. Specifically, the MRI apparatus acquires MR signals by using a radio frequency (RF) multi-coil including RF coils, a permanent magnet, and gradient coils.
In detail, by using a pulse sequence for generating RF signals, RF signals may be applied to an object via the RF multi-coil and an MR image may be reconstructed by sampling MR signals corresponding to the applied RF signals.
In current MRI apparatuses, an average scan time is about 30 minutes. In general, an MRI apparatus includes an elongated, narrow tube (hereinafter, referred to as an ‘MRI tube’). A patient that will undergo MRI is placed inside the MRI tube and needs to remain still therein during the scanning process. Thus, patients having serious disease or having a fear of enclosed spaces (claustrophobia) have difficulty undergoing an MRI scan, and most patients may feel bored and uncomfortable during a long scan time.
To solve these problems, an image processing apparatus and method that are capable of shortening MRI scan time are needed.
Various approaches have been attempted to reduce an MRI scan time. For example, without sampling all lines in K-space, by undersampling MR signals along equally spaced lines in K-space and calibrating undersampled K-space data, a final MR image may be reconstructed.
A Generalized Auto-calibrating Partially Parallel Acquisition (GRAPPA) technique is a K-space based imaging method. In the GRAPPA technique spatial correlation coefficients or convolution kernel coefficients that are spatial interaction values between a calibration signal and an adjacent measured source signal are calculated by self-calibration, and unmeasured signals may be estimated by using the calculated spatial correlation coefficients, so that the MRI scan time may be reduced.
In detail, in the GRAPPA technique, unacquired K-space lines for each channel may be restored by using measured line data that are undersampled data and additionally acquired auto-calibrating (ACS) line data.
Multi-parameter mapping may be used to shorten an MRI scan time and quantify a plurality of parameters. To quantify parameters for an object, instead of traditional techniques to repeatedly acquire data, multi-parameter mapping may employ a pseudorandomized acquisition technique causing MR signals from a material or tissue to have unique signal evolutions respectively, like fingerprints. For example, MR fingerprinting technique may be used as one of the multi-parameter mapping methods.
The acquired MR signals may be converted into a quantitative map of parameters by performing matching between a predicted signal evolution and a prestored signal model.
In multi-parameter mapping, pseudorandomized flip angle and repetition time (TR) may be used so that MR signals from a material or tissue to have unique signal evolutions respectively.
However, use of pseudorandomized flip angle and repetition time (TR) results in loss of signal-to-noise ratio (SNR), and for a fat signal or a signal having a large off-resonance, parameters may not be quantified properly. Another problem is that MR signals from different materials may not clearly be distinguished from each other. If MR signals are not clearly distinguished from each other, the probability of errors during matching between a signal evolution and a signal model increases, and thus reliability on quantified parameters may be reduced.